When our hearts beat, pulse waves, also known as aortic pulses, are generated and propagated along the arterial network of the cardiovascular system. When these pulse waves reach sites with impedance mismatch such as vessel bifurcations and site with changes in vessel radius, wave reflections will be produced. These wave reflections could be captured at various measurement sites. For example, the wave reflections measured at the wrist of a human body is known as radial pulse waveform. Previous researches have proposed using the ratio of late systolic peak to early systolic peak of the radial pulse waveform as the arterial stiffness index. Nonetheless, an accurate location of early systolic peak and late systolic peak is not a trivial task. First of all, the early systolic peak and late systolic peak may be too close to each other. Secondly, the amplitude of the late systolic component is too small compared to that of the early systolic component. These two scenarios increase the difficulties of an accurate separation of early systolic component and the late systolic component. Furthermore, depending on the choice of measurement site and the subject of interest, the radial pulse waveform measured may be too noisy and weak for post-processing.
Different methodologies have been proposed to deal with the aforesaid limitations, for instance curve fitting method and waveform classification based on nth derivative of the radial pulse waveform. Nonetheless, these methodologies suffer from different inherit limitations. For curve fitting method, it usually takes more than 100 iterations in order to accurately decompose a radial pulse waveform. Such high computation demand limits the practical application of such technologies. For derivative-based classification, it is well known that derivative operation would amplify the high frequency noises. As such, the classification is very sensitive to noise and prone to error.